Overview

Dataset statistics

Number of variables34
Number of observations162
Missing cells203
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.2 KiB
Average record size in memory272.8 B

Variable types

Categorical7
Text3
DateTime4
Numeric20

Alerts

status has constant value ""Constant
seas has constant value ""Constant
yr has constant value ""Constant
network is highly imbalanced (94.6%)Imbalance
stopDate has 160 (98.8%) missing valuesMissing
seas has 2 (1.2%) missing valuesMissing
yr has 2 (1.2%) missing valuesMissing
Criteria1 has 2 (1.2%) missing valuesMissing
Criteria2 has 2 (1.2%) missing valuesMissing
Criteria3 has 2 (1.2%) missing valuesMissing
Ca has 2 (1.2%) missing valuesMissing
Mg has 2 (1.2%) missing valuesMissing
K has 2 (1.2%) missing valuesMissing
Na has 2 (1.2%) missing valuesMissing
NH4 has 2 (1.2%) missing valuesMissing
NO3 has 2 (1.2%) missing valuesMissing
Cl has 2 (1.2%) missing valuesMissing
SO4 has 2 (1.2%) missing valuesMissing
H has 2 (1.2%) missing valuesMissing
Conduc has 2 (1.2%) missing valuesMissing
svol has 2 (1.2%) missing valuesMissing
ppt has 2 (1.2%) missing valuesMissing
fullChemLab has 2 (1.2%) missing valuesMissing
daysSample has 2 (1.2%) missing valuesMissing
startDate_y has 2 (1.2%) missing valuesMissing
lastDate has 2 (1.2%) missing valuesMissing
siteID has unique valuesUnique
siteName has unique valuesUnique
latitude has unique valuesUnique
longitude has unique valuesUnique

Reproduction

Analysis started2024-04-18 17:55:21.057103
Analysis finished2024-04-18 17:56:15.865878
Duration54.81 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

network
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
NTN
161 
TN
 
1

Length

Max length3
Median length3
Mean length2.9938272
Min length2

Characters and Unicode

Total characters485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowNTN
2nd rowNTN
3rd rowNTN
4th rowNTN
5th rowNTN

Common Values

ValueCountFrequency (%)
NTN 161
99.4%
TN 1
 
0.6%

Length

2024-04-18T12:56:16.012596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:56:16.223846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ntn 161
99.4%
tn 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N 323
66.6%
T 162
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 323
66.6%
T 162
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 323
66.6%
T 162
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 323
66.6%
T 162
33.4%

siteID
Text

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:16.633110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters648
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)100.0%

Sample

1st rowAK01
2nd rowAK02
3rd rowAK96
4th rowAK97
5th rowAL10
ValueCountFrequency (%)
ak01 1
 
0.6%
ga41 1
 
0.6%
ca76 1
 
0.6%
ar27 1
 
0.6%
ak96 1
 
0.6%
ak97 1
 
0.6%
al10 1
 
0.6%
al99 1
 
0.6%
ar02 1
 
0.6%
ar03 1
 
0.6%
Other values (152) 152
93.8%
2024-04-18T12:56:17.210364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68
 
10.5%
9 55
 
8.5%
1 46
 
7.1%
A 44
 
6.8%
N 41
 
6.3%
M 32
 
4.9%
2 31
 
4.8%
C 27
 
4.2%
4 26
 
4.0%
Y 25
 
3.9%
Other values (24) 253
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 68
 
10.5%
9 55
 
8.5%
1 46
 
7.1%
A 44
 
6.8%
N 41
 
6.3%
M 32
 
4.9%
2 31
 
4.8%
C 27
 
4.2%
4 26
 
4.0%
Y 25
 
3.9%
Other values (24) 253
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 68
 
10.5%
9 55
 
8.5%
1 46
 
7.1%
A 44
 
6.8%
N 41
 
6.3%
M 32
 
4.9%
2 31
 
4.8%
C 27
 
4.2%
4 26
 
4.0%
Y 25
 
3.9%
Other values (24) 253
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 68
 
10.5%
9 55
 
8.5%
1 46
 
7.1%
A 44
 
6.8%
N 41
 
6.3%
M 32
 
4.9%
2 31
 
4.8%
C 27
 
4.2%
4 26
 
4.0%
Y 25
 
3.9%
Other values (24) 253
39.0%

siteName
Text

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:17.552635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length56
Median length38
Mean length19.006173
Min length5

Characters and Unicode

Total characters3079
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)100.0%

Sample

1st rowPoker Creek
2nd rowJuneau
3rd rowToolik Field Station
4th rowKatmai National Park - King Salmon
5th rowBlack Belt Research & Extension Center
ValueCountFrequency (%)
national 33
 
7.9%
station 11
 
2.6%
research 9
 
2.2%
wildlife 8
 
1.9%
park 7
 
1.7%
center 6
 
1.4%
forest 6
 
1.4%
lake 6
 
1.4%
refuge 6
 
1.4%
farm 5
 
1.2%
Other values (274) 319
76.7%
2024-04-18T12:56:18.080719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 309
 
10.0%
e 273
 
8.9%
254
 
8.2%
n 204
 
6.6%
o 202
 
6.6%
i 190
 
6.2%
t 183
 
5.9%
r 172
 
5.6%
l 170
 
5.5%
s 111
 
3.6%
Other values (46) 1011
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 309
 
10.0%
e 273
 
8.9%
254
 
8.2%
n 204
 
6.6%
o 202
 
6.6%
i 190
 
6.2%
t 183
 
5.9%
r 172
 
5.6%
l 170
 
5.5%
s 111
 
3.6%
Other values (46) 1011
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 309
 
10.0%
e 273
 
8.9%
254
 
8.2%
n 204
 
6.6%
o 202
 
6.6%
i 190
 
6.2%
t 183
 
5.9%
r 172
 
5.6%
l 170
 
5.5%
s 111
 
3.6%
Other values (46) 1011
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 309
 
10.0%
e 273
 
8.9%
254
 
8.2%
n 204
 
6.6%
o 202
 
6.6%
i 190
 
6.2%
t 183
 
5.9%
r 172
 
5.6%
l 170
 
5.5%
s 111
 
3.6%
Other values (46) 1011
32.8%

status
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
A
162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters162
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 162
100.0%

Length

2024-04-18T12:56:18.286006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:56:18.417811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
a 162
100.0%

Most occurring characters

ValueCountFrequency (%)
A 162
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 162
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 162
100.0%
Distinct140
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum1978-07-05 00:00:00
Maximum2021-11-16 00:00:00
2024-04-18T12:56:18.566269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:18.764452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stopDate
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing160
Missing (%)98.8%
Memory size1.4 KiB
Minimum2023-12-31 00:00:00
Maximum2024-03-26 00:00:00
2024-04-18T12:56:18.918393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:19.057418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

county
Text

Distinct145
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:19.390199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.3271605
Min length4

Characters and Unicode

Total characters1187
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)82.1%

Sample

1st rowFairbanks North Star
2nd rowJuneau
3rd rowNorth Slope Borough
4th rowBristol Bay
5th rowDallas
ValueCountFrequency (%)
washington 4
 
2.3%
franklin 4
 
2.3%
suffolk 3
 
1.7%
san 3
 
1.7%
larimer 2
 
1.1%
los 2
 
1.1%
north 2
 
1.1%
essex 2
 
1.1%
bernardino 2
 
1.1%
prince 2
 
1.1%
Other values (144) 150
85.2%
2024-04-18T12:56:19.959851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 126
 
10.6%
n 108
 
9.1%
e 100
 
8.4%
o 94
 
7.9%
r 80
 
6.7%
i 61
 
5.1%
t 59
 
5.0%
l 57
 
4.8%
s 47
 
4.0%
u 33
 
2.8%
Other values (40) 422
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 126
 
10.6%
n 108
 
9.1%
e 100
 
8.4%
o 94
 
7.9%
r 80
 
6.7%
i 61
 
5.1%
t 59
 
5.0%
l 57
 
4.8%
s 47
 
4.0%
u 33
 
2.8%
Other values (40) 422
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 126
 
10.6%
n 108
 
9.1%
e 100
 
8.4%
o 94
 
7.9%
r 80
 
6.7%
i 61
 
5.1%
t 59
 
5.0%
l 57
 
4.8%
s 47
 
4.0%
u 33
 
2.8%
Other values (40) 422
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 126
 
10.6%
n 108
 
9.1%
e 100
 
8.4%
o 94
 
7.9%
r 80
 
6.7%
i 61
 
5.1%
t 59
 
5.0%
l 57
 
4.8%
s 47
 
4.0%
u 33
 
2.8%
Other values (40) 422
35.6%

state
Categorical

Distinct43
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
NY
15 
CO
12 
NC
 
7
ME
 
7
MI
 
7
Other values (38)
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters324
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)4.3%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAL

Common Values

ValueCountFrequency (%)
NY 15
 
9.3%
CO 12
 
7.4%
NC 7
 
4.3%
ME 7
 
4.3%
MI 7
 
4.3%
WY 6
 
3.7%
WI 6
 
3.7%
CA 6
 
3.7%
VA 5
 
3.1%
MN 5
 
3.1%
Other values (33) 86
53.1%

Length

2024-04-18T12:56:20.166870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ny 15
 
9.3%
co 12
 
7.4%
nc 7
 
4.3%
mi 7
 
4.3%
me 7
 
4.3%
wy 6
 
3.7%
wi 6
 
3.7%
ca 6
 
3.7%
az 5
 
3.1%
ma 5
 
3.1%
Other values (33) 86
53.1%

Most occurring characters

ValueCountFrequency (%)
A 44
13.6%
N 41
12.7%
M 32
9.9%
C 27
 
8.3%
Y 25
 
7.7%
I 22
 
6.8%
O 19
 
5.9%
W 16
 
4.9%
T 14
 
4.3%
K 11
 
3.4%
Other values (14) 73
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 44
13.6%
N 41
12.7%
M 32
9.9%
C 27
 
8.3%
Y 25
 
7.7%
I 22
 
6.8%
O 19
 
5.9%
W 16
 
4.9%
T 14
 
4.3%
K 11
 
3.4%
Other values (14) 73
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 44
13.6%
N 41
12.7%
M 32
9.9%
C 27
 
8.3%
Y 25
 
7.7%
I 22
 
6.8%
O 19
 
5.9%
W 16
 
4.9%
T 14
 
4.3%
K 11
 
3.4%
Other values (14) 73
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 44
13.6%
N 41
12.7%
M 32
9.9%
C 27
 
8.3%
Y 25
 
7.7%
I 22
 
6.8%
O 19
 
5.9%
W 16
 
4.9%
T 14
 
4.3%
K 11
 
3.4%
Other values (14) 73
22.5%

latitude
Real number (ℝ)

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.144677
Minimum25.39
Maximum68.6257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:20.342276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum25.39
5-th percentile30.81889
Q136.193275
median40.33545
Q343.4348
95-th percentile47.370125
Maximum68.6257
Range43.2357
Interquartile range (IQR)7.241525

Descriptive statistics

Standard deviation5.983882
Coefficient of variation (CV)0.14905792
Kurtosis4.6081383
Mean40.144677
Median Absolute Deviation (MAD)3.49745
Skewness1.1082741
Sum6503.4377
Variance35.806844
MonotonicityNot monotonic
2024-04-18T12:56:20.537640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.155 1
 
0.6%
44.2118 1
 
0.6%
40.868 1
 
0.6%
42.7339 1
 
0.6%
42.2994 1
 
0.6%
43.9731 1
 
0.6%
44.9226 1
 
0.6%
43.4336 1
 
0.6%
43.1463 1
 
0.6%
42.4014 1
 
0.6%
Other values (152) 152
93.8%
ValueCountFrequency (%)
25.39 1
0.6%
27.3801 1
0.6%
28.7486 1
0.6%
29.7603 1
0.6%
29.931 1
0.6%
30.2613 1
0.6%
30.4294 1
0.6%
30.7404 1
0.6%
30.7819 1
0.6%
31.5217 1
0.6%
ValueCountFrequency (%)
68.6257 1
0.6%
65.155 1
0.6%
58.6794 1
0.6%
58.5139 1
0.6%
48.5102 1
0.6%
48.4132 1
0.6%
47.9464 1
0.6%
47.8471 1
0.6%
47.3841 1
0.6%
47.1046 1
0.6%

longitude
Real number (ℝ)

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.536893
Minimum-156.6664
Maximum-67.6308
Zeros0
Zeros (%)0.0%
Negative162
Negative (%)100.0%
Memory size1.4 KiB
2024-04-18T12:56:20.729326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-156.6664
5-th percentile-122.15735
Q1-105.58707
median-87.04025
Q3-78.547025
95-th percentile-71.01453
Maximum-67.6308
Range89.0356
Interquartile range (IQR)27.04005

Descriptive statistics

Standard deviation17.318329
Coefficient of variation (CV)-0.18919507
Kurtosis1.1503409
Mean-91.536893
Median Absolute Deviation (MAD)10.38095
Skewness-1.0611429
Sum-14828.977
Variance299.9245
MonotonicityNot monotonic
2024-04-18T12:56:20.922898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-147.491 1
 
0.6%
-122.256 1
 
0.6%
-73.8782 1
 
0.6%
-76.6597 1
 
0.6%
-79.3964 1
 
0.6%
-74.2231 1
 
0.6%
-74.4806 1
 
0.6%
-74.5002 1
 
0.6%
-77.5482 1
 
0.6%
-76.6589 1
 
0.6%
Other values (152) 152
93.8%
ValueCountFrequency (%)
-156.6664 1
0.6%
-149.6069 1
0.6%
-147.491 1
0.6%
-134.7843 1
0.6%
-123.19 1
0.6%
-123.086 1
0.6%
-122.4798 1
0.6%
-122.256 1
0.6%
-122.21 1
0.6%
-121.157 1
0.6%
ValueCountFrequency (%)
-67.6308 1
0.6%
-68.0134 1
0.6%
-68.2608 1
0.6%
-69.6647 1
0.6%
-70.0241 1
0.6%
-70.0645 1
0.6%
-70.1751 1
0.6%
-70.2118 1
0.6%
-71.0098 1
0.6%
-71.1044 1
0.6%

elevation
Real number (ℝ)

Distinct148
Distinct (%)91.9%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean681.93168
Minimum1
Maximum3520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:21.109293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q1174
median307
Q3753
95-th percentile2524
Maximum3520
Range3519
Interquartile range (IQR)579

Descriptive statistics

Standard deviation842.5884
Coefficient of variation (CV)1.2355906
Kurtosis1.9131573
Mean681.93168
Median Absolute Deviation (MAD)217
Skewness1.7010968
Sum109791
Variance709955.21
MonotonicityNot monotonic
2024-04-18T12:56:21.308027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
2.5%
1 3
 
1.9%
2477 2
 
1.2%
2524 2
 
1.2%
150 2
 
1.2%
46 2
 
1.2%
212 2
 
1.2%
25 2
 
1.2%
134 2
 
1.2%
229 2
 
1.2%
Other values (138) 138
85.2%
ValueCountFrequency (%)
1 3
1.9%
2 4
2.5%
3 1
 
0.6%
6 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
25 2
1.2%
32 1
 
0.6%
41 1
 
0.6%
45 1
 
0.6%
ValueCountFrequency (%)
3520 1
0.6%
3287 1
0.6%
3269 1
0.6%
3181 1
0.6%
3159 1
0.6%
2915 1
0.6%
2633 1
0.6%
2524 2
1.2%
2502 1
0.6%
2477 2
1.2%

stateName
Categorical

Distinct43
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
New York
15 
Colorado
12 
North Carolina
 
7
Maine
 
7
Michigan
 
7
Other values (38)
114 

Length

Max length14
Median length12
Mean length8.2469136
Min length4

Characters and Unicode

Total characters1336
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)4.3%

Sample

1st rowAlaska
2nd rowAlaska
3rd rowAlaska
4th rowAlaska
5th rowAlabama

Common Values

ValueCountFrequency (%)
New York 15
 
9.3%
Colorado 12
 
7.4%
North Carolina 7
 
4.3%
Maine 7
 
4.3%
Michigan 7
 
4.3%
Wyoming 6
 
3.7%
Wisconsin 6
 
3.7%
California 6
 
3.7%
Virginia 5
 
3.1%
Minnesota 5
 
3.1%
Other values (33) 86
53.1%

Length

2024-04-18T12:56:21.499724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 21
 
10.9%
york 15
 
7.8%
colorado 12
 
6.2%
carolina 8
 
4.1%
north 7
 
3.6%
maine 7
 
3.6%
michigan 7
 
3.6%
virginia 7
 
3.6%
wyoming 6
 
3.1%
wisconsin 6
 
3.1%
Other values (35) 97
50.3%

Most occurring characters

ValueCountFrequency (%)
a 159
 
11.9%
i 130
 
9.7%
o 124
 
9.3%
n 124
 
9.3%
s 87
 
6.5%
e 83
 
6.2%
r 83
 
6.2%
l 48
 
3.6%
t 39
 
2.9%
M 32
 
2.4%
Other values (34) 427
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 159
 
11.9%
i 130
 
9.7%
o 124
 
9.3%
n 124
 
9.3%
s 87
 
6.5%
e 83
 
6.2%
r 83
 
6.2%
l 48
 
3.6%
t 39
 
2.9%
M 32
 
2.4%
Other values (34) 427
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 159
 
11.9%
i 130
 
9.7%
o 124
 
9.3%
n 124
 
9.3%
s 87
 
6.5%
e 83
 
6.2%
r 83
 
6.2%
l 48
 
3.6%
t 39
 
2.9%
M 32
 
2.4%
Other values (34) 427
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 159
 
11.9%
i 130
 
9.7%
o 124
 
9.3%
n 124
 
9.3%
s 87
 
6.5%
e 83
 
6.2%
r 83
 
6.2%
l 48
 
3.6%
t 39
 
2.9%
M 32
 
2.4%
Other values (34) 427
32.0%

siteClass
Categorical

Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
R
73 
I
61 
S
13 
U
11 
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters162
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd row
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
R 73
45.1%
I 61
37.7%
S 13
 
8.0%
U 11
 
6.8%
4
 
2.5%

Length

2024-04-18T12:56:21.656054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:56:21.801835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
r 73
46.2%
i 61
38.6%
s 13
 
8.2%
u 11
 
7.0%

Most occurring characters

ValueCountFrequency (%)
R 73
45.1%
I 61
37.7%
S 13
 
8.0%
U 11
 
6.8%
4
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 73
45.1%
I 61
37.7%
S 13
 
8.0%
U 11
 
6.8%
4
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 73
45.1%
I 61
37.7%
S 13
 
8.0%
U 11
 
6.8%
4
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 73
45.1%
I 61
37.7%
S 13
 
8.0%
U 11
 
6.8%
4
 
2.5%

seas
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
Annual
160 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters960
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnnual
2nd rowAnnual
3rd rowAnnual
4th rowAnnual
5th rowAnnual

Common Values

ValueCountFrequency (%)
Annual 160
98.8%
(Missing) 2
 
1.2%

Length

2024-04-18T12:56:21.965420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:56:22.094370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
annual 160
100.0%

Most occurring characters

ValueCountFrequency (%)
n 320
33.3%
A 160
16.7%
u 160
16.7%
a 160
16.7%
l 160
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 320
33.3%
A 160
16.7%
u 160
16.7%
a 160
16.7%
l 160
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 320
33.3%
A 160
16.7%
u 160
16.7%
a 160
16.7%
l 160
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 320
33.3%
A 160
16.7%
u 160
16.7%
a 160
16.7%
l 160
16.7%

yr
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
2022.0
160 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters960
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022.0
2nd row2022.0
3rd row2022.0
4th row2022.0
5th row2022.0

Common Values

ValueCountFrequency (%)
2022.0 160
98.8%
(Missing) 2
 
1.2%

Length

2024-04-18T12:56:22.230379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T12:56:22.366119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2022.0 160
100.0%

Most occurring characters

ValueCountFrequency (%)
2 480
50.0%
0 320
33.3%
. 160
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 480
50.0%
0 320
33.3%
. 160
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 480
50.0%
0 320
33.3%
. 160
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 480
50.0%
0 320
33.3%
. 160
 
16.7%

Criteria1
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)14.4%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean85.3625
Minimum59
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:22.500035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile77
Q182
median85
Q389.25
95-th percentile94
Maximum100
Range41
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation5.7805655
Coefficient of variation (CV)0.067717856
Kurtosis1.7993897
Mean85.3625
Median Absolute Deviation (MAD)3
Skewness-0.38767618
Sum13658
Variance33.414937
MonotonicityNot monotonic
2024-04-18T12:56:22.653026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
84 19
11.7%
88 17
10.5%
82 17
10.5%
86 14
 
8.6%
92 13
 
8.0%
90 12
 
7.4%
77 10
 
6.2%
94 7
 
4.3%
83 7
 
4.3%
87 6
 
3.7%
Other values (13) 38
23.5%
ValueCountFrequency (%)
59 1
 
0.6%
75 3
 
1.9%
76 2
 
1.2%
77 10
6.2%
78 5
 
3.1%
79 5
 
3.1%
80 1
 
0.6%
81 6
 
3.7%
82 17
10.5%
83 7
4.3%
ValueCountFrequency (%)
100 1
 
0.6%
98 2
 
1.2%
96 4
 
2.5%
94 7
4.3%
92 13
8.0%
91 1
 
0.6%
90 12
7.4%
89 2
 
1.2%
88 17
10.5%
87 6
 
3.7%

Criteria2
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)5.0%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean98.84375
Minimum90
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:22.797479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile96
Q199
median99
Q3100
95-th percentile100
Maximum100
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4859801
Coefficient of variation (CV)0.015033627
Kurtosis13.103309
Mean98.84375
Median Absolute Deviation (MAD)1
Skewness-3.1634791
Sum15815
Variance2.2081368
MonotonicityNot monotonic
2024-04-18T12:56:22.944345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
99 78
48.1%
100 49
30.2%
98 20
 
12.3%
96 5
 
3.1%
97 3
 
1.9%
94 2
 
1.2%
92 2
 
1.2%
90 1
 
0.6%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
90 1
 
0.6%
92 2
 
1.2%
94 2
 
1.2%
96 5
 
3.1%
97 3
 
1.9%
98 20
 
12.3%
99 78
48.1%
100 49
30.2%
ValueCountFrequency (%)
100 49
30.2%
99 78
48.1%
98 20
 
12.3%
97 3
 
1.9%
96 5
 
3.1%
94 2
 
1.2%
92 2
 
1.2%
90 1
 
0.6%

Criteria3
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)16.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean90.88125
Minimum62
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:23.096555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile80.95
Q187
median92
Q395.25
95-th percentile98.05
Maximum100
Range38
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation6.1187123
Coefficient of variation (CV)0.067326454
Kurtosis2.1858162
Mean90.88125
Median Absolute Deviation (MAD)4
Skewness-1.0558897
Sum14541
Variance37.43864
MonotonicityNot monotonic
2024-04-18T12:56:23.264503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
98 15
 
9.3%
92 15
 
9.3%
93 12
 
7.4%
89 11
 
6.8%
91 11
 
6.8%
96 10
 
6.2%
95 10
 
6.2%
94 9
 
5.6%
88 7
 
4.3%
97 7
 
4.3%
Other values (16) 53
32.7%
ValueCountFrequency (%)
62 1
 
0.6%
75 1
 
0.6%
76 1
 
0.6%
78 2
 
1.2%
79 2
 
1.2%
80 1
 
0.6%
81 4
2.5%
82 4
2.5%
83 4
2.5%
84 5
3.1%
ValueCountFrequency (%)
100 5
 
3.1%
99 3
 
1.9%
98 15
9.3%
97 7
4.3%
96 10
6.2%
95 10
6.2%
94 9
5.6%
93 12
7.4%
92 15
9.3%
91 11
6.8%

Ca
Real number (ℝ)

MISSING 

Distinct132
Distinct (%)82.5%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean10.373426
Minimum-9
Maximum90.0384
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:23.449871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile1.87995
Q13.87195
median6.9969
Q312.2508
95-th percentile30.64692
Maximum90.0384
Range99.0384
Interquartile range (IQR)8.37885

Descriptive statistics

Standard deviation11.540159
Coefficient of variation (CV)1.1124733
Kurtosis16.910741
Mean10.373426
Median Absolute Deviation (MAD)3.735
Skewness3.3931645
Sum1659.7482
Variance133.17528
MonotonicityNot monotonic
2024-04-18T12:56:23.636993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.221 3
 
1.9%
2.7888 3
 
1.9%
3.0876 3
 
1.9%
3.8844 3
 
1.9%
6.6732 2
 
1.2%
8.6652 2
 
1.2%
3.984 2
 
1.2%
9.7608 2
 
1.2%
12.0516 2
 
1.2%
5.0298 2
 
1.2%
Other values (122) 136
84.0%
ValueCountFrequency (%)
-9 1
0.6%
0.996 1
0.6%
1.3944 1
0.6%
1.494 1
0.6%
1.5936 2
1.2%
1.6434 2
1.2%
1.8924 1
0.6%
1.992 1
0.6%
2.1414 1
0.6%
2.1912 1
0.6%
ValueCountFrequency (%)
90.0384 1
0.6%
63.9432 1
0.6%
50.547 1
0.6%
38.4954 1
0.6%
36.105 1
0.6%
35.5572 1
0.6%
34.7106 1
0.6%
31.0254 1
0.6%
30.627 1
0.6%
30.5772 1
0.6%

Mg
Real number (ℝ)

MISSING 

Distinct62
Distinct (%)38.8%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.9498389
Minimum-9
Maximum32.32818
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:23.825410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile0.736227
Q11.295595
median1.97424
Q33.310965
95-th percentile7.695423
Maximum32.32818
Range41.32818
Interquartile range (IQR)2.01537

Descriptive statistics

Standard deviation3.9323859
Coefficient of variation (CV)1.333085
Kurtosis25.053592
Mean2.9498389
Median Absolute Deviation (MAD)0.86373
Skewness4.1809369
Sum471.97422
Variance15.463659
MonotonicityNot monotonic
2024-04-18T12:56:24.238928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.89198 8
 
4.9%
2.22102 8
 
4.9%
0.98712 8
 
4.9%
2.30328 7
 
4.3%
3.78396 6
 
3.7%
1.56294 6
 
3.7%
1.72746 6
 
3.7%
1.97424 6
 
3.7%
0.90486 5
 
3.1%
1.15164 5
 
3.1%
Other values (52) 95
58.6%
ValueCountFrequency (%)
-9 1
 
0.6%
0.32904 1
 
0.6%
0.49356 1
 
0.6%
0.57582 1
 
0.6%
0.65808 4
2.5%
0.74034 3
 
1.9%
0.8226 3
 
1.9%
0.90486 5
3.1%
0.98712 8
4.9%
1.06938 4
2.5%
ValueCountFrequency (%)
32.32818 1
0.6%
21.96342 1
0.6%
19.49562 1
0.6%
15.71166 1
0.6%
15.13584 1
0.6%
10.6938 1
0.6%
10.2825 1
0.6%
8.55504 1
0.6%
7.65018 1
0.6%
7.56792 1
0.6%

K
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)35.0%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.7249135
Minimum-9
Maximum2.65928
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:24.419818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile0.3055615
Q10.48583
median0.652035
Q30.89495
95-th percentile2.050714
Maximum2.65928
Range11.65928
Interquartile range (IQR)0.40912

Descriptive statistics

Standard deviation0.91351872
Coefficient of variation (CV)1.2601762
Kurtosis81.486544
Mean0.7249135
Median Absolute Deviation (MAD)0.191775
Skewness-7.3257431
Sum115.98616
Variance0.83451645
MonotonicityNot monotonic
2024-04-18T12:56:24.616385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53697 7
 
4.3%
0.63925 7
 
4.3%
0.46026 7
 
4.3%
0.69039 7
 
4.3%
0.33241 7
 
4.3%
0.5114 7
 
4.3%
0.58811 6
 
3.7%
0.43469 6
 
3.7%
0.56254 5
 
3.1%
0.40912 5
 
3.1%
Other values (46) 96
59.3%
ValueCountFrequency (%)
-9 1
 
0.6%
0.20456 1
 
0.6%
0.23013 2
 
1.2%
0.2557 3
1.9%
0.28127 1
 
0.6%
0.30684 1
 
0.6%
0.33241 7
4.3%
0.35798 3
1.9%
0.38355 1
 
0.6%
0.40912 5
3.1%
ValueCountFrequency (%)
2.65928 1
0.6%
2.53143 1
0.6%
2.48029 1
0.6%
2.32687 1
0.6%
2.27573 1
0.6%
2.17345 2
1.2%
2.14788 1
0.6%
2.0456 2
1.2%
1.96889 1
0.6%
1.58534 1
0.6%

Na
Real number (ℝ)

MISSING 

Distinct108
Distinct (%)67.5%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean7.3232531
Minimum-9
Maximum95.787
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:24.810012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile1.085325
Q11.816125
median2.93625
Q36.709875
95-th percentile28.825275
Maximum95.787
Range104.787
Interquartile range (IQR)4.89375

Descriptive statistics

Standard deviation13.389752
Coefficient of variation (CV)1.8283885
Kurtosis21.902432
Mean7.3232531
Median Absolute Deviation (MAD)1.50075
Skewness4.3446539
Sum1171.7205
Variance179.28546
MonotonicityNot monotonic
2024-04-18T12:56:25.024191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.436 4
 
2.5%
2.8275 4
 
2.5%
1.6965 4
 
2.5%
1.9575 3
 
1.9%
2.61 3
 
1.9%
1.0875 3
 
1.9%
3.4365 3
 
1.9%
2.001 3
 
1.9%
1.7835 3
 
1.9%
2.5665 3
 
1.9%
Other values (98) 127
78.4%
ValueCountFrequency (%)
-9 1
 
0.6%
0.8265 1
 
0.6%
0.87 1
 
0.6%
0.957 1
 
0.6%
1.0005 1
 
0.6%
1.044 3
1.9%
1.0875 3
1.9%
1.131 2
1.2%
1.1745 2
1.2%
1.218 1
 
0.6%
ValueCountFrequency (%)
95.787 1
0.6%
85.521 1
0.6%
70.47 1
0.6%
50.3295 1
0.6%
46.9365 1
0.6%
37.5405 1
0.6%
34.887 1
0.6%
31.842 1
0.6%
28.6665 1
0.6%
26.187 1
0.6%

NH4
Real number (ℝ)

MISSING 

Distinct136
Distinct (%)85.0%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean15.568128
Minimum-9
Maximum66.02904
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:25.226868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile4.60152
Q18.37144
median12.39084
Q320.52666
95-th percentile34.489224
Maximum66.02904
Range75.02904
Interquartile range (IQR)12.15522

Descriptive statistics

Standard deviation10.478205
Coefficient of variation (CV)0.6730549
Kurtosis3.0372988
Mean15.568128
Median Absolute Deviation (MAD)5.65488
Skewness1.3199737
Sum2490.9005
Variance109.79278
MonotonicityNot monotonic
2024-04-18T12:56:25.447743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2668 3
 
1.9%
10.8108 3
 
1.9%
17.07552 3
 
1.9%
5.544 2
 
1.2%
4.60152 2
 
1.2%
10.2564 2
 
1.2%
33.76296 2
 
1.2%
8.64864 2
 
1.2%
9.31392 2
 
1.2%
17.79624 2
 
1.2%
Other values (126) 137
84.6%
ValueCountFrequency (%)
-9 1
0.6%
0.49896 1
0.6%
1.21968 1
0.6%
1.60776 1
0.6%
2.71656 1
0.6%
2.82744 1
0.6%
2.93832 1
0.6%
4.60152 2
1.2%
4.76784 1
0.6%
4.82328 1
0.6%
ValueCountFrequency (%)
66.02904 1
0.6%
48.01104 1
0.6%
44.352 1
0.6%
38.64168 1
0.6%
38.41992 1
0.6%
35.92512 1
0.6%
35.03808 1
0.6%
34.59456 1
0.6%
34.48368 1
0.6%
33.76296 2
1.2%

NO3
Real number (ℝ)

MISSING 

Distinct146
Distinct (%)91.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean9.0675837
Minimum-9
Maximum21.09804
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:25.666132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile3.826036
Q16.718145
median8.93602
Q311.617632
95-th percentile14.833954
Maximum21.09804
Range30.09804
Interquartile range (IQR)4.8994875

Descriptive statistics

Standard deviation3.8328599
Coefficient of variation (CV)0.42269915
Kurtosis2.7280067
Mean9.0675837
Median Absolute Deviation (MAD)2.38724
Skewness-0.29976169
Sum1450.8134
Variance14.690815
MonotonicityNot monotonic
2024-04-18T12:56:25.881957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.27475 3
 
1.9%
13.12982 2
 
1.2%
12.72657 2
 
1.2%
6.59717 2
 
1.2%
7.87144 2
 
1.2%
11.06518 2
 
1.2%
9.41992 2
 
1.2%
11.32326 2
 
1.2%
8.98441 2
 
1.2%
7.11333 2
 
1.2%
Other values (136) 139
85.8%
ValueCountFrequency (%)
-9 1
0.6%
1.37105 1
0.6%
1.7743 1
0.6%
1.87108 1
0.6%
1.90334 1
0.6%
1.98399 1
0.6%
2.50015 1
0.6%
3.58086 1
0.6%
3.83894 1
0.6%
3.8712 1
0.6%
ValueCountFrequency (%)
21.09804 1
0.6%
18.2269 1
0.6%
18.09786 1
0.6%
17.0978 1
0.6%
16.95263 1
0.6%
16.69455 1
0.6%
15.9687 1
0.6%
15.33963 1
0.6%
14.80734 1
0.6%
14.43635 1
0.6%

Cl
Real number (ℝ)

MISSING 

Distinct125
Distinct (%)78.1%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean8.3755427
Minimum-9
Maximum115.77384
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:26.093217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile1.15661
Q11.80544
median2.93384
Q37.348705
95-th percentile37.062298
Maximum115.77384
Range124.77384
Interquartile range (IQR)5.543265

Descriptive statistics

Standard deviation16.13774
Coefficient of variation (CV)1.9267694
Kurtosis21.926866
Mean8.3755427
Median Absolute Deviation (MAD)1.49513
Skewness4.3300534
Sum1340.0868
Variance260.42664
MonotonicityNot monotonic
2024-04-18T12:56:26.315602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.80544 5
 
3.1%
1.15661 3
 
1.9%
2.51069 3
 
1.9%
1.26945 3
 
1.9%
1.18482 3
 
1.9%
1.43871 3
 
1.9%
1.77723 2
 
1.2%
2.76458 2
 
1.2%
3.21594 2
 
1.2%
2.36964 2
 
1.2%
Other values (115) 132
81.5%
ValueCountFrequency (%)
-9 1
 
0.6%
0.78988 1
 
0.6%
0.90272 1
 
0.6%
0.93093 1
 
0.6%
1.07198 1
 
0.6%
1.10019 1
 
0.6%
1.15661 3
1.9%
1.18482 3
1.9%
1.21303 2
1.2%
1.26945 3
1.9%
ValueCountFrequency (%)
115.77384 1
0.6%
102.65619 1
0.6%
83.44518 1
0.6%
56.92778 1
0.6%
56.27895 1
0.6%
42.06111 1
0.6%
38.50665 1
0.6%
38.02708 1
0.6%
37.01152 1
0.6%
33.5699 1
0.6%

SO4
Real number (ℝ)

MISSING 

Distinct138
Distinct (%)86.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean7.0920852
Minimum-9
Maximum16.4557
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:26.509394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile2.6631155
Q15.02003
median6.62394
Q39.050635
95-th percentile12.808367
Maximum16.4557
Range25.4557
Interquartile range (IQR)4.030605

Descriptive statistics

Standard deviation3.3303365
Coefficient of variation (CV)0.46958494
Kurtosis3.1047262
Mean7.0920852
Median Absolute Deviation (MAD)2.072585
Skewness-0.096897901
Sum1134.7336
Variance11.091141
MonotonicityNot monotonic
2024-04-18T12:56:26.713299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.60279 4
 
2.5%
6.20734 3
 
1.9%
3.81189 2
 
1.2%
6.62394 2
 
1.2%
8.9569 2
 
1.2%
3.5411 2
 
1.2%
7.12386 2
 
1.2%
9.08188 2
 
1.2%
5.91572 2
 
1.2%
4.31181 2
 
1.2%
Other values (128) 137
84.6%
ValueCountFrequency (%)
-9 1
0.6%
1.83304 1
0.6%
1.85387 1
0.6%
2.12466 1
0.6%
2.20798 1
0.6%
2.22881 1
0.6%
2.56209 1
0.6%
2.60375 1
0.6%
2.66624 2
1.2%
2.68707 1
0.6%
ValueCountFrequency (%)
16.4557 1
0.6%
16.18491 1
0.6%
15.14341 1
0.6%
15.06009 1
0.6%
15.01843 1
0.6%
14.62266 1
0.6%
14.60183 1
0.6%
13.56033 1
0.6%
12.76879 1
0.6%
12.47717 1
0.6%

H
Real number (ℝ)

MISSING 

Distinct150
Distinct (%)93.8%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean3.4112062
Minimum-9
Maximum9.036
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:26.899054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile0.59675
Q11.9495
median3.277
Q34.7915
95-th percentile6.96305
Maximum9.036
Range18.036
Interquartile range (IQR)2.842

Descriptive statistics

Standard deviation2.165165
Coefficient of variation (CV)0.63472122
Kurtosis5.90899
Mean3.4112062
Median Absolute Deviation (MAD)1.404
Skewness-0.75969167
Sum545.793
Variance4.6879395
MonotonicityNot monotonic
2024-04-18T12:56:27.094142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.467 3
 
1.9%
5.715 2
 
1.2%
3.846 2
 
1.2%
3.273 2
 
1.2%
4.529 2
 
1.2%
4.742 2
 
1.2%
4.335 2
 
1.2%
2.46 2
 
1.2%
3.055 2
 
1.2%
3.936 1
 
0.6%
Other values (140) 140
86.4%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
-9 1
0.6%
0.301 1
0.6%
0.34 1
0.6%
0.413 1
0.6%
0.452 1
0.6%
0.531 1
0.6%
0.532 1
0.6%
0.592 1
0.6%
0.597 1
0.6%
0.615 1
0.6%
ValueCountFrequency (%)
9.036 1
0.6%
8.77 1
0.6%
8.75 1
0.6%
8.166 1
0.6%
7.727 1
0.6%
7.447 1
0.6%
7.396 1
0.6%
7.211 1
0.6%
6.95 1
0.6%
6.531 1
0.6%

Conduc
Real number (ℝ)

MISSING 

Distinct158
Distinct (%)98.8%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean6.14445
Minimum-9
Maximum19.667
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.6%
Memory size1.4 KiB
2024-04-18T12:56:27.290617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile3.4803
Q14.531
median5.5635
Q36.8585
95-th percentile10.46565
Maximum19.667
Range28.667
Interquartile range (IQR)2.3275

Descriptive statistics

Standard deviation2.9407963
Coefficient of variation (CV)0.47861017
Kurtosis9.1059785
Mean6.14445
Median Absolute Deviation (MAD)1.1655
Skewness0.98289004
Sum983.112
Variance8.6482828
MonotonicityNot monotonic
2024-04-18T12:56:27.486357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.381 2
 
1.2%
7.618 2
 
1.2%
3.488 1
 
0.6%
4.622 1
 
0.6%
4.709 1
 
0.6%
5.979 1
 
0.6%
3.598 1
 
0.6%
5.315 1
 
0.6%
4.695 1
 
0.6%
8.07 1
 
0.6%
Other values (148) 148
91.4%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
-9 1
0.6%
2.708 1
0.6%
2.765 1
0.6%
2.877 1
0.6%
2.929 1
0.6%
3.124 1
0.6%
3.405 1
0.6%
3.448 1
0.6%
3.482 1
0.6%
3.488 1
0.6%
ValueCountFrequency (%)
19.667 1
0.6%
18.581 1
0.6%
15.642 1
0.6%
15.462 1
0.6%
14.35 1
0.6%
14.308 1
0.6%
11.604 1
0.6%
11.143 1
0.6%
10.43 1
0.6%
10.194 1
0.6%

svol
Real number (ℝ)

MISSING 

Distinct160
Distinct (%)100.0%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean56569.451
Minimum6493.1
Maximum140951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:27.680119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6493.1
5-th percentile14666.28
Q136510.575
median58917.7
Q375072.975
95-th percentile99587.91
Maximum140951
Range134457.9
Interquartile range (IQR)38562.4

Descriptive statistics

Standard deviation26747.387
Coefficient of variation (CV)0.47282387
Kurtosis-0.31572948
Mean56569.451
Median Absolute Deviation (MAD)19085.8
Skewness0.13103752
Sum9051112.2
Variance7.154227 × 108
MonotonicityNot monotonic
2024-04-18T12:56:27.888085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17184.5 1
 
0.6%
54383.9 1
 
0.6%
40164.1 1
 
0.6%
87613.8 1
 
0.6%
74841.1 1
 
0.6%
59907.7 1
 
0.6%
83005.2 1
 
0.6%
52988.7 1
 
0.6%
44299.3 1
 
0.6%
101925.1 1
 
0.6%
Other values (150) 150
92.6%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
6493.1 1
0.6%
8833.2 1
0.6%
8945.9 1
0.6%
12243.1 1
0.6%
12716.7 1
0.6%
13546.8 1
0.6%
14098.2 1
0.6%
14584.2 1
0.6%
14670.6 1
0.6%
14829.4 1
0.6%
ValueCountFrequency (%)
140951 1
0.6%
128304.5 1
0.6%
114816.6 1
0.6%
102217.6 1
0.6%
101925.1 1
0.6%
101336.2 1
0.6%
101213.3 1
0.6%
99949.1 1
0.6%
99568.9 1
0.6%
93615.2 1
0.6%

ppt
Real number (ℝ)

MISSING 

Distinct159
Distinct (%)99.4%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean98.915944
Minimum13.513
Maximum246.034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:28.084127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum13.513
5-th percentile31.21385
Q175.04625
median103.701
Q3125.96475
95-th percentile161.48055
Maximum246.034
Range232.521
Interquartile range (IQR)50.9185

Descriptive statistics

Standard deviation40.547575
Coefficient of variation (CV)0.40991951
Kurtosis0.18766233
Mean98.915944
Median Absolute Deviation (MAD)25.4745
Skewness0.004770421
Sum15826.551
Variance1644.1058
MonotonicityNot monotonic
2024-04-18T12:56:28.279943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125.806 2
 
1.2%
121.717 1
 
0.6%
77.457 1
 
0.6%
135.853 1
 
0.6%
122.465 1
 
0.6%
105.913 1
 
0.6%
136.652 1
 
0.6%
84.154 1
 
0.6%
80.888 1
 
0.6%
32.619 1
 
0.6%
Other values (149) 149
92.0%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
13.513 1
0.6%
16.486 1
0.6%
23.616 1
0.6%
24.105 1
0.6%
25.848 1
0.6%
26.375 1
0.6%
27.89 1
0.6%
29.235 1
0.6%
31.318 1
0.6%
31.877 1
0.6%
ValueCountFrequency (%)
246.034 1
0.6%
187.746 1
0.6%
175.081 1
0.6%
174.473 1
0.6%
166.04 1
0.6%
163.933 1
0.6%
163.271 1
0.6%
162.916 1
0.6%
161.405 1
0.6%
157.203 1
0.6%

fullChemLab
Real number (ℝ)

MISSING 

Distinct38
Distinct (%)23.8%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.675
Minimum11
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:28.461706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile18
Q132.75
median38.5
Q343
95-th percentile48
Maximum51
Range40
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation8.6413879
Coefficient of variation (CV)0.23562067
Kurtosis0.27779838
Mean36.675
Median Absolute Deviation (MAD)5.5
Skewness-0.88417283
Sum5868
Variance74.673585
MonotonicityNot monotonic
2024-04-18T12:56:28.658956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
39 15
 
9.3%
45 12
 
7.4%
38 9
 
5.6%
43 9
 
5.6%
36 9
 
5.6%
37 8
 
4.9%
40 8
 
4.9%
44 6
 
3.7%
41 6
 
3.7%
35 5
 
3.1%
Other values (28) 73
45.1%
ValueCountFrequency (%)
11 1
 
0.6%
13 1
 
0.6%
15 1
 
0.6%
16 1
 
0.6%
17 3
1.9%
18 2
1.2%
19 1
 
0.6%
20 1
 
0.6%
21 2
1.2%
22 1
 
0.6%
ValueCountFrequency (%)
51 1
 
0.6%
49 5
3.1%
48 4
 
2.5%
47 4
 
2.5%
46 5
3.1%
45 12
7.4%
44 6
3.7%
43 9
5.6%
42 5
3.1%
41 6
3.7%

daysSample
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)6.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean370.0125
Minimum357
Maximum372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-04-18T12:56:28.816923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum357
5-th percentile364
Q1371
median371
Q3371
95-th percentile371
Maximum372
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5500956
Coefficient of variation (CV)0.0068919174
Kurtosis6.6655396
Mean370.0125
Median Absolute Deviation (MAD)0
Skewness-2.5995995
Sum59202
Variance6.5029874
MonotonicityNot monotonic
2024-04-18T12:56:29.185224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
371 128
79.0%
364 9
 
5.6%
365 7
 
4.3%
370 5
 
3.1%
372 4
 
2.5%
366 3
 
1.9%
357 1
 
0.6%
368 1
 
0.6%
363 1
 
0.6%
359 1
 
0.6%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
357 1
 
0.6%
359 1
 
0.6%
363 1
 
0.6%
364 9
 
5.6%
365 7
 
4.3%
366 3
 
1.9%
368 1
 
0.6%
370 5
 
3.1%
371 128
79.0%
372 4
 
2.5%
ValueCountFrequency (%)
372 4
 
2.5%
371 128
79.0%
370 5
 
3.1%
368 1
 
0.6%
366 3
 
1.9%
365 7
 
4.3%
364 9
 
5.6%
363 1
 
0.6%
359 1
 
0.6%
357 1
 
0.6%

startDate_y
Date

MISSING 

Distinct120
Distinct (%)75.0%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
Minimum2021-12-27 14:58:00
Maximum2022-01-11 16:00:00
2024-04-18T12:56:29.360464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:29.566943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

lastDate
Date

MISSING 

Distinct121
Distinct (%)75.6%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
Minimum2022-12-27 21:10:00
Maximum2023-01-04 21:45:00
2024-04-18T12:56:29.760188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:30.000064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-04-18T12:56:11.982516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:22.559278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.292405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:27.611649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:30.127782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:32.621215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:34.895507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:37.458836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.214862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:42.755080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.087464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:47.732469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:50.337660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:53.023860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:55.772276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:58.450428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:00.914811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:03.709837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:06.679406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.275462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.095990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:22.680098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.405961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:27.727076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:30.242953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:32.726618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.006484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:37.563277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.313729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:42.862257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.196500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:47.845421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:50.446854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:53.136372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:55.894645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:58.573129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:01.025564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:03.823416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:06.859004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.397018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.214138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:22.799082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.541175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:27.841010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:30.361112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:32.833672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.119312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:37.678117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.419318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:42.966048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.305065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:47.958550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:50.558057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:53.255187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.024747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:58.696607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:01.133189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:03.934755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:07.107197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.727013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.343941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:22.979188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.661767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:27.968945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:30.489905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:32.955761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.245855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:37.800810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.534134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:43.085764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.427709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:48.080812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:50.770916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:53.389124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.173620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:58.828784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:01.257722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:04.067419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:07.254591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.862220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.461338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:23.125443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.767478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:28.089811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:30.602128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:33.062406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.419212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:38.134197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.637744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:43.188678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.540673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:48.201810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:50.914952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:53.509293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.290156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:58.939354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:01.731573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:04.253610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:07.379030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.980303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.579477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:23.250233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.877492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:28.220407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:30.710760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:33.172948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.543928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:38.250038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.802360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:43.297649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.660324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:48.402026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:51.070101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:53.631871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.509951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:59.053575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:01.910324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:04.441867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:07.501704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:10.102107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.710271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:23.379246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.998358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:28.350478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:31.027269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:33.296560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.667811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:38.383496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:41.020252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:43.416531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.794108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:48.546677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:51.206669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:54.001585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.660876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:59.186799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:02.081104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:04.783169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:07.633925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:10.231770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.822100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:23.480639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:26.102587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:28.464755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:31.140015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:33.400398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.784054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:38.495094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:41.147254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:43.517784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:45.911335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:48.667808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:51.323368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:54.108962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.796913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:59.294995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:02.195293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:04.898801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:07.750206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:10.347441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:12.938371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:23.582994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:26.215398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:28.579913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:31.250854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:33.506245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:35.901869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:38.614078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:41.318343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:43.620233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:46.038119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:48.784397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:51.439231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:54.213680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:56.918131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:59.402551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:02.304859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-18T12:56:03.458580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:06.341711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.018721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:11.716787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:14.303823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:25.168221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:27.492036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:29.995790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:32.501080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:34.771031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:37.330164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:40.091341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:42.570092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:44.957922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:47.601827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:50.194943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:52.894886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:55.599032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:55:58.313462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:00.733533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:03.586468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:06.487964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:09.151802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-18T12:56:11.849368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-04-18T12:56:14.623386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T12:56:15.219326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

networksiteIDsiteNamestatusstartDate_xstopDatecountystatelatitudelongitudeelevationstateNamesiteClassseasyrCriteria1Criteria2Criteria3CaMgKNaNH4NO3ClSO4HConducsvolpptfullChemLabdaysSamplestartDate_ylastDate
0NTNAK01Poker CreekA12/29/92NaNFairbanks North StarAK65.1550-147.4910230.0AlaskaIAnnual2022.088.098.093.01.39440.329040.255700.95702.938322.500151.156612.603755.9843.48817184.5032.61929.0371.0Dec 28 2021 10:50PMJan 3 2023 11:00PM
1NTNAK02JuneauA6/22/04NaNJuneauAK58.5139-134.784325.0AlaskaIAnnual2022.092.099.096.00.99600.904860.204564.87200.498961.371055.670212.228814.6563.124140951.00246.03446.0371.0Dec 28 2021 11:19PMJan 3 2023 8:42PM
2NTNAK96Toolik Field StationA10/12/17NaNNorth Slope BoroughAK68.6257-149.6069730.0AlaskaAnnual2022.092.099.093.04.83060.740340.383551.52252.716563.871201.720812.874545.6893.66813546.8031.31835.0371.0Dec 28 2021 8:45PMJan 3 2023 6:40PM
3NTNAK97Katmai National Park - King SalmonA11/2/09NaNBristol BayAK58.6794-156.666450.0AlaskaIAnnual2022.082.099.083.01.64342.221020.4602610.35301.219681.9033413.230495.436637.2115.24647153.9987.41742.0372.0Dec 28 2021 7:30PMJan 4 2023 7:54PM
4NTNAL10Black Belt Research & Extension CenterA8/31/83NaNDallasAL32.4583-87.242258.0AlabamaIAnnual2022.087.0100.089.03.88442.221020.894958.482510.367286.516529.591408.623625.4955.87792996.80153.01037.0371.0Dec 28 2021 3:00PMJan 3 2023 3:45PM
5NTNAL99Sand Mountain Research & Extension CenterA10/2/84NaNDeKalbAL34.2886-85.9699349.0AlabamaRAnnual2022.086.099.091.03.83461.562941.483064.263014.857926.516524.428977.352993.3574.953102217.60163.93339.0371.0Dec 28 2021 2:49PMJan 3 2023 2:37PM
6NTNAR02Warren 2WSWA5/25/82NaNBradleyAR33.6050-92.097276.0ArkansasRAnnual2022.089.0100.093.08.16722.303280.664828.004010.311849.307018.6040512.477176.5316.81191256.80157.20337.0371.0Dec 28 2021 2:20PMJan 3 2023 3:30PM
7NTNAR03Caddo ValleyA12/30/83NaNClarkAR34.1795-93.099271.0ArkansasRAnnual2022.077.0100.091.09.96002.879102.173457.090510.810809.597357.4756513.560335.3336.85287344.20139.54829.0371.0Dec 28 2021 2:00PMJan 3 2023 2:30PM
8NTNAR16Buffalo National River-Buffalo PointA7/13/82NaNMarionAR36.0842-92.5868311.0ArkansasIAnnual2022.077.098.085.09.06361.562941.329642.827512.474007.984352.821008.290343.1624.98170891.30126.54729.0371.0Dec 28 2021 6:00PMJan 3 2023 4:30PM
9NTNAR27FayettevilleA5/13/80NaNWashingtonAR36.1011-94.1737381.0ArkansasSAnnual2022.090.0100.097.014.94001.974241.073944.437019.459449.629614.2879210.123382.2396.21982589.00128.65138.0371.0Dec 28 2021 5:15PMJan 3 2023 3:00PM
networksiteIDsiteNamestatusstartDate_xstopDatecountystatelatitudelongitudeelevationstateNamesiteClassseasyrCriteria1Criteria2Criteria3CaMgKNaNH4NO3ClSO4HConducsvolpptfullChemLabdaysSamplestartDate_ylastDate
152NTNWI36Trout LakeA1/22/80NaNVilasWI46.0512-89.6541509.0WisconsinIAnnual2022.086.0100.089.014.04362.879100.818241.653027.3319211.549081.607976.623941.1896.39450860.387.45640.0363.0Jan 4 2022 2:35PMJan 2 2023 6:10PM
153NTNWI37SpoonerA6/3/80NaNWashburnWI45.8228-91.8744331.0WisconsinRAnnual2022.082.099.086.021.81244.606560.715962.436038.4199213.758892.313227.748760.8938.66544871.178.41140.0371.0Dec 28 2021 2:17PMJan 3 2023 4:45PM
154NTNWV04Babcock State ParkA9/6/83NaNFayetteWV37.9796-80.9525753.0West VirginiaRAnnual2022.086.0100.092.04.53180.904860.536971.21809.036727.935961.438716.207345.1054.54999568.9162.91645.0371.0Dec 28 2021 1:45PMJan 3 2023 1:50PM
155NTNWV18ParsonsA7/5/78NaNTuckerWV39.0897-79.6622505.0West VirginiaIAnnual2022.0100.0100.0100.05.87641.151640.562541.305010.256407.871441.579767.394654.7424.73482193.4133.90951.0371.0Dec 28 2021 12:00PMJan 3 2023 12:00PM
156NTNWY00Snowy RangeA4/22/86NaNAlbanyWY41.3762-106.26003269.0WyomingIAnnual2022.077.098.085.08.66521.727460.332411.60959.258486.693951.213034.665922.7294.02417834.594.78437.0371.0Dec 28 2021 6:42PMJan 3 2023 5:55PM
157NTNWY02Sinks CanyonA8/21/84NaNFremontWY42.7336-108.84982164.0WyomingIAnnual2022.081.0100.082.06.67321.151640.255701.000511.420645.113210.902723.520272.2233.64020781.950.68832.0365.0Jan 4 2022 6:15PMJan 4 2023 9:45PM
158NTNWY06PinedaleA1/26/82NaNSubletteWY42.9290-109.78752388.0WyomingIAnnual2022.082.099.094.015.48782.796840.588113.219016.576569.823172.708166.603112.7355.78512716.735.66336.0371.0Dec 28 2021 7:00PMJan 3 2023 4:55PM
159NTNWY94Grand Tetons National ParkA9/27/11NaNTetonWY43.8333-110.70082105.0WyomingIAnnual2022.078.098.083.012.40022.221022.659285.002513.970887.339154.259714.415961.9365.33325556.249.72233.0364.0Jan 4 2022 4:30PMJan 3 2023 4:30PM
160NTNWY95Brooklyn LakeA9/22/92NaNAlbanyWY41.3647-106.24083181.0WyomingIAnnual2022.084.0100.088.010.85641.974240.460262.56658.648645.629371.297664.353472.7544.23727298.498.99643.0371.0Dec 28 2021 9:03PMJan 3 2023 7:01PM
161NTNWY97South Pass CityA4/30/85NaNFremontWY42.4944-108.83202524.0WyomingIAnnual2022.083.099.087.011.25481.727460.357981.78359.313925.790671.100194.020192.2914.02014670.644.25233.0359.0Jan 3 2022 9:00PMDec 28 2022 5:15PM